Associate Computational Biologist, Target Discovery

A major obstacle for treating cancer is a lack of precision medicines. Many potential targeted therapies fail to transition from preclinical models to patients due to incomplete knowledge of the drug’s mechanism of action and/or absence of robust biomarkers to identify relevant patient populations. The Target Discovery arm of the Cancer Dependency Map project aims to provide the oncology community with potential drug targets that have a high likelihood of success.

Our strategy is to develop a more comprehensive understanding of each target’s function in cancer by performing computational analyses that establish associations between the target and the molecular features that predict sensitivity to its perturbation. This information helps guide us in nominating targets that are most likely to translate to patient tumors.

You will be responsible for improving the computational framework for systematically identifying and prioritizing cancer-related gene targets. As part of this effort, you will work with some of the largest experimental cancer biology datasets in the world, including functional genomic (CRISPR, RNAi) and small molecule screens on hundreds of cancer cell lines, and their corresponding multi-omics profiles, e.g. RNA-Seq, whole exome sequencing, and methylation. Experience with software pipeline development is a plus.

Associate Computational Biologist, Flagship

As part of the Dependency Map project, several large-scale ‘Flagship’ efforts are now underway. These highly collaborative projects seek to combine insights from large-scale preclinical datasets of cancer vulnerabilities with the expertise of clinician scientists in particular disease areas (e.g. pediatric cancer, GI cancer).

As part of the Flagship DepMap teams, you will work closely with cancer biologists and clinicians to derive new translational insights from large omics and functional screening datasets. These projects will require you to help design experimental and computational research strategies, analyze a broad range of complex, high-dimensional data, and apply a variety of statistical and machine learning tools.